Label-free detection of small and large molecule interactions, and activities in biological systems

ABSTRACT

A system for quantitative detection and analysis of the interactions of molecules with molecular receptors on the surfaces of biological cells based on detecting a mechanical deformation in the membrane of a cell associated with the molecular interactions, which works for both large and small molecules. The mechanical deformation can be detected with high precision in real time from an optical image of the cell with a differential detection method. The system can be also used to detect the electrical activities, such as ion channel opening and closing, as well as action potential propagation in neuronal cells.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.14/968,331 filed Dec. 14, 2015 having the same title which is anon-provisional application of expired U.S. Provisional PatentApplication No. 62/091,828, filed Dec. 15, 2014, entitled “LABEL-FREEDETECTION OF SMALL AND LARGE MOLECULE INTERACTIONS, AND ACTIVITIES INBIOLOGICAL SYSTEMS,” to the same inventors herein and claims thepriority benefit of that filing date. Application Ser. Nos. 14/968,331and 62/091,828 are incorporated by reference.

TECHNICAL FIELD

The present invention relates to the field of biomedical research,screening and development of drugs, and discovery, validation anddetection of biomarkers for diseases, and, more particularly, to amethod based on nanometer-precision tracking of molecularbinding-induced mechanical deformation in the cell membrane via opticalimaging, which is label-free, real time and non-invasive.

BACKGROUND

Measuring molecular binding or interactions is a basic task forunderstanding many biological processes and for developing relevantapplications. A particularly important application is to determine thebinding properties of drugs with their corresponding membrane proteinreceptors, which are the largest type of drug targets.¹ Molecularbinding is quantified by kinetic constants, which have, thus, become acriterion in preclinical drug screening.^(2,3)

Advances in structural biology have led to an exponential growth in thenumber of membrane proteins with determined 3D structures. However, asnoted above, in order to understand the cellular functions of membraneproteins, it is also necessary to determine the interaction kinetics ofthe membrane proteins with various molecules. This is because cellsperform many functions, including communication, via the interactions oftheir membrane proteins with molecules in the extracellular medium. Acapability to quantify membrane protein interactions with molecules isalso critical for discovering and validating drugs because most drugtargets are membrane proteins. Despite the importance, developing such acapability that can measure the interactions of molecules with membraneproteins in the natural lipid environment has been a difficult task.

One traditional method for determining the kinetic constants is toextract molecular receptors from cells, immobilize them on a solidsurface after purification, and then expose them to the drug forbinding.⁴ Although useful, such methods can be problematic, especiallywhen the receptors are membrane proteins,⁵ which currently count formore than a half of the drug targets.⁶ Due to their unique amphiphilicstructures, it is difficult to ensure that the purified membraneproteins retain their native structures and functions.⁴ Because of theheterogeneous nature of cells, it is also important to study each of theindividual cells. These capabilities, if developed, will benefit notonly drug discovery, but also drug resistance study, which is a commonbut difficult problem in medicine.⁷⁻⁹

Typically, methods for studying molecular interactions use radioactiveor fluorescent labels. These end point assays do not provide kineticconstants that are needed to quantify the membrane interactions andfunctions. To determine the kinetic information, the current practiceinvolves extracting membrane proteins from cells, purifying them fromthe extracts, immobilizing the purified proteins on a solid surface, andthen exposing them to a ligand for kinetic study. The procedures are notonly laborious, but also prone to alteration of the native functions ofmembrane proteins, especially integral membrane proteins that arepermanently attached to the membrane. Furthermore, the isolation ofmembrane proteins from their native cellular environment prevents onefrom studying the allosteric effect in the molecular interactions, andexamining heterogeneous nature of cells. A more serious limitation ofthe existing technologies is that the detection signal diminishes withthe mass of the molecule, making them difficult for detecting smallmolecules, which play many important roles in cellular functions, andrepresent the vast majority of the existing drugs.

Various methods have been developed for in situ measurement ofdrug-receptor binding. A popular method is kinetic exclusion assay,which measures the concentration of free drugs remaining in thesupernatant after the binding equilibrium in cell suspension is achievedwith a labeled detection technology.¹⁵⁻¹⁷ This is an end-point assay,and not suitable for extracting the kinetic constants, including theassociation and dissociation constants. Furthermore, the use of labelsis not only labor intensive but may also affect the native bindingbehaviors of the molecular receptors. Label-free technologies, such asquartz crystal microbalance, have been developed for studying drugbinding properties.¹⁸ Although useful, they lack spatial resolutionrequired to study the variability between different individual cells,map heterogeneous distribution of receptors in the cell membrane, anddistinguish non-specific binding onto the sensor surface from specificbinding to the receptors on the cells. Another label-free detectiontechnology is surface plasmon resonance (SPR) technique that can monitorthe lectinglycoprotein interactions in single cells.¹⁹ However, like thequartz crystal microbalance, SPR signal is proportional to the mass ofthe molecule (e.g., drug), which has limited sensitivity for detectingdrug molecules with typically small molecular masses.

The present invention overcomes the shortcomings discussed above and,for the first time, discloses a system that can detect the binding ofboth large and small molecules with the molecular receptors in singlecells, and analyze the corresponding binding kinetic constants. Themethod can be applied to measure the binding of drugs with theirmembrane receptor targets in the native cellular membranes, and toanalyze cell-to-cell variability of the binding kinetics by measuringmechanical deformation of cells upon interactions of the cellularmembrane proteins with molecules in the extracellular medium. Acapability of real time analysis of the interactions in single cells byanalyzing the mechanical deformation with sub-nm resolution is alsoprovided for the first time. For small molecules, the present methodrepresents the first kinetic measurement while the equilibrium constantsextracted from the present method are consistent with those obtainedwith endpoint radioactive labeling assay. The imaging capability allowsrevelation of cell-to-cell variability of difference cells, andregion-to-region variability within the same cell. The detectionprinciple of the present invention may also be used to monitor theelectrical activities in neurons.

BRIEF SUMMARY OF THE DISCLOSURE

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features ofthe claimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

A system for quantitative detection and analysis of the interactions ofmolecules with molecular receptors on the surfaces of biological cellsbased on detecting a mechanical deformation in the membrane of a cellassociated with the molecular interactions is disclosed. An imagingapparatus captures a time sequence of images of a biological objectincludes an injector coupled to introduce a substance that interactswith the biological object. A processor is coupled to receive data fromthe imaging apparatus and adapted to determine the mechanicaldeformation of the biological object associated with the interaction ofthe substance with the biological object from the images, the processorbeing further adapted to analyze the interaction from the mechanicaldeformation.

BRIEF DESCRIPTION OF THE DRAWINGS

While the novel features of the invention are set forth withparticularity in the appended claims, the invention, both as toorganization and content, will be better understood and appreciated,along with other objects and features thereof, from the followingdetailed description taken in conjunction with the drawings, in which:

FIG. 1A schematically shows illumination of the setup for edge trackingdetection.

FIG. 1B illustrates differential optical detection for accurate trackingof cell edge change induced by analyte receptor interaction.

FIG. 1C graphically illustrates a typical binding curve as determinedfrom the cell edge movement.

FIG. 1D graphically illustrates an example where the root mean square ofthe fixed cell edge change is about 0.46 nm.

FIG. 1E is a cartoon that illustrates cell edge changes over time duringthe binding process. i, ii, and iii correspond to the stages marked inFIG. 1C.

FIG. 2A shows WGA interaction with glycoproteins featuring phasecontrast images of fixed CP-D cells for 20 g/ml WGA binding.

FIG. 2B shows WGA interaction with glycoproteins featuring phasecontrast images of fixed CP-D cells for 5 g/ml WGA binding.

FIG. 2C graphically illustrates an example for averaged cell edgemovement over the whole cell for 20 g/ml WGA.

FIG. 2D graphically illustrates an example for averaged cell edgemovement over the whole cell for 5 g/ml WGA.

FIG. 3A shows acetylcholine interaction with nicotinic acetylcholinereceptors in cells, specifically a phase contrast image of the fixedhuman a4β2 transfected SH-EP1 cells, where the white arrow marks thecell under analysis.

FIG. 3B shows averaged cell edge movement over the whole cell andfitting results during the binding process for acetylcholine ofdifferent concentrations.

FIG. 3C graphically illustrates an example wherein the equilibriumconstant (K_(D)) was determined to be 2.601 μM by plotting the edgemovement vs. acetylcholine concentration and fitting the data with theLangmuir isotherm.

FIG. 3D shows acetylcholine interaction with nicotinic acetylcholinereceptors in cells, specifically a phase contrast image of the fixedhuman a4β2 transfected SH-EP1 cells, where the white arrow marks thecell under analysis.

FIG. 3E shows averaged cell edge movement over the whole cell andfitting results during the binding process for acetylcholine ofdifferent concentrations.

FIG. 3F graphically illustrates an example wherein the equilibriumconstant (K_(D)) was determined to be ˜26 nM by plotting the edgemovement vs. acetylcholine concentration and fitting the data with theLangmuir isotherm.

FIG. 3G illustrates phase contrast images of wild type SH-EP1 cells.

FIG. 3H represents response of wild type SH-EP1 cells to 500 nMacetylcholine.

FIG. 4A and FIG. 4C show heterogeneity of small molecule interactionswith cell membrane receptors. Phase contrast images of fixed human a4β2transfected SH-EP1 cells, where the numbers mark the cells underanalysis. Scale bar: 10.

FIG. 4B graphically illustrates an example of binding kinetics of cells1 and 2 (50 μM Acetylcholine).

FIG. 4D graphically illustrates an example of binding kinetics of cells3 and 4 (50 μetylcholine).

FIG. 4E graphically illustrates an example of binding kinetics atdifferent locations (50 μetylcholine).

FIG. 4F shows a differential image of the cell shown in the insets ofFIG. 4E obtained by subtracting the image recorded at 0 s from that at68 s.

FIG. 5A and FIG. 5C show phase contrast images of fixed human α4β2transfected SH-EP1 cells.

FIG. 5B and FIG. 5D graphically illustrate binding kinetics of cells.

FIG. 5E graphically illustrates binding kinetics at different locationsof cell in f (100 nM Acetylcholine).

FIG. 5F shows a phase contrast image of fixed human α4β2 transfectedSH-EP1 cell.

FIG. 6A-FIG. 6E schematically show a mechanism for trapping cells ontothe holes of a microplate.

7A shows a bright field image of a floating cell clamped with glassmicropipette.

FIG. 7B shows a cell modulated by an external applied AC field.

FIG. 8A schematically shows the setup of z-modulation FFT microscopy.

FIG. 8B schematically shows the application of temporal Fast FourierTransform (FFT) applied to the bright field image sequences over a timewindow.

FIG. 8C schematically shows periodically modulated image intensity of apixel, and its corresponding FFT spectrum, where a peak is located atthe frequency of modulation.

FIG. 8D and FIG. 8E illustrate individual 42 nm polystyrenenanoparticles that are invisible in bright field image, but are clearlyvisible in the modulation image.

FIG. 8F and FIG. 8G contrast a z-modulation FFT image showing superiorcontrast compared with the traditional bright field image obtained withsame camera.

FIG. 9A and FIG. 9B show images from a preliminary test of WGA bindingkinetics on a SHEP1 cell with the z-modulation FFT microscope are shown.

FIG. 9C illustrates binding kinetic plots of 10, 20 and 50 μg/mI WGA.

In the drawings, identical reference numbers identify similar elementsor components. The sizes and relative positions of elements in thedrawings are not necessarily drawn to scale. For example, the shapes ofvarious elements and angles are not drawn to scale, and some of theseelements are arbitrarily enlarged and positioned to improve drawinglegibility. Further, the particular shapes of the elements as drawn, arenot intended to convey any information regarding the actual shape of theparticular elements, and have been solely selected for ease ofrecognition in the drawings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following disclosure describes several embodiments for metabolicanalyzers that are based on detection of several metabolic signatures.Several features of methods and systems in accordance with exampleembodiments are set forth and described in the figures. It will beappreciated that methods and systems in accordance with other exampleembodiments can include additional procedures or features different thanthose shown in the figures. Example embodiments are described hereinwith respect to a portable metabolic analyzer system. However, it willbe understood that these examples are for the purpose of illustratingthe principles, and that the invention is not so limited. Additionally,methods and systems in accordance with several example embodiments maynot include all of the features shown in the figures.

Unless the context requires otherwise, throughout the specification andclaims which follow, the word “comprise” and variations thereof, suchas, “comprises” and “comprising” are to be construed in an open,inclusive sense that is as “including, but not limited to.”

Reference throughout this specification to “one example” or “an exampleembodiment,” “one embodiment,” “an embodiment” or combinations and/orvariations of these terms means that a particular feature, structure orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present disclosure. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

Definitions

Generally, as used herein, the following terms have the followingmeanings when used within the context of sample collection or analysis:

As used herein, “plurality” is understood to mean more than one. Forexample, a plurality refers to at least 3, 4, 5, 70, or more.

As used herein, “neuron” refers to a nerve cell that receives and sendselectrical signals over long distances within the body. A neuronreceives electrical input signals from sensory cells (called sensoryneurons) and from other neurons. The neuron sends electrical outputsignals to muscle neurons (called motoneurons or motor neurons) and toother neurons.

As used herein, “MADMI” refers to a method for mechanically amplifieddetection of molecular interactions that provides amplified molecularinteraction signals, allowing measurement of the binding kinetics ofmolecules with membrane proteins on single live cells, and analysis ofthe heterogeneous nature of the binding kinetics between differentcells, and different regions of a single cell.

As used herein, “Nicotinic acetylcholine receptors,” or “nAChRs,” areneuron receptor proteins that signal for muscular contraction upon achemical stimulus.

Principle of the invention

The basic detection principle of the present invention is to measure adeformation (shape or size) in the cell that accompanies the binding ofmolecules with the molecular receptors on the cell surface. Thisdeformation occurs because the law of thermodynamics predicts that whenmolecules bind to the surface the surface tension changes. According tothermodynamics, the relative surface concentration of molecules bound onthe membrane surface is given by

$\begin{matrix}{{\Gamma = {- ( \frac{\delta\gamma}{\delta\mu} )_{T,P,}}},} & (1)\end{matrix}$where γ is the surface tension and μ is the chemical potential of themolecules. For ideal solutions, the chemical potential is related to thebulk concentration, c, according todμ=k _(B) TdΓ(lnc),  (2)where k_(B) is the Boltzmann constant, and T is temperature.Substituting Eq. 2 into Eq. 1, we havedγ=kBTdΓ(dc/c),  (3)If we assume the molecular binding follows the Langmuir isotherm, Eq. 3can be simplified as

$\begin{matrix}{{{d\;\gamma} = {{RT}( \frac{\Gamma_{0}}{\Gamma_{0} - \Gamma} )}},} & (4)\end{matrix}$where Γ₀ is the surface concentration at the full (maximum) coverage.When Γ«Γ₀, Eq. 4 can be further simplified and takes the form of,dγ=k_(B)TdΓ.  (5)Eq. 5 clearly shows that the surface tension of the cell membranechanges with the molecular binding.

Some of the receptor proteins are ion channels, which open and close toallow different ions to move in and out of a cell. Associated with theion re-distribution is an electrical potential change across the cell'smembrane, particularly neurons, giving rise to electrical activity, suchas action potential that is critical for information processing inbrains. The change in the ion distribution or electrical activities alsoleads to a mechanical deformation in the cell, as described the Lippmannequation, given by

$\begin{matrix}{{q = ( \frac{\delta\gamma}{\delta\; V} )_{T,p,\mu}},} & (6)\end{matrix}$where q is surface charge density and V is the electrical potential. Eq.6 predicts that for a given charge density, a change in the electricalpotential results in a change in the surface tension, thus a mechanicaldeformation in the cell. This is the principle for the detection ofelectrical activities of neurons, such as action potential, by measuringand analyzing the cell deformation.

Note that Eq. 5 was derived with many assumptions, but the basic conceptthat a molecular binding event causes a change in the surface tension isexpected to be a general phenomenon. However, the actual amount ofconformational change in the cell depends on the biding strengths, aswell as the geometry and mechanical properties of the cells. Forpractical applications, one typically wants to determine the bindingkinetic constants, which can be obtained from the time dependence of thecell membrane deformation (mechanical response) during the association(binding) and dissociation (unbinding) processes. In other words, thereis no need to relate the measured mechanical response to the microscopicscaled processes.

Description of the System

Referring now jointly to FIG. 1A-FIG.1E, an overview of edge trackingdetection employing the methods disclosed herein is illustrated. Todetect a small amount of molecules or a weak molecular binding event, itis critical to be able to measure a small amount of mechanicaldeformation in the cell. This task is accomplished in the present systemby detecting and tracking the edge movement of a cell image.

Referring now particularly to FIG. 1A, there shown schematically is anexample of an illumination setup. The setup includes a cell 5 withreceptors 15 on a glass slide 22. Analytes 20 are introduced and adifferential interference contrast (DIC) microscope 10 is positioned totrack activity. A traditional bright field optical microscopy may beused, but better image contrast can be obtained with phase contrastmicroscopy, differential interference contrast (DIC) microscopy, andvarious interference optical microscopic techniques. In one usefulexample an inverted phase contrast microscope with 40× phase 2 objective10 was used for detection. The DIC microscope 10 transmits timesequenced images to a sensor 41, which in turn passes data representingthe time sequenced images to a processor 43 for analysis. The sensor 41may be any commercially available device such as a CCD video camera orequivalent. The processor may advantageously include a CPU or equivalentdevice for processing digital data, for example. The data for timesequenced images may be analyzed to determine differences in the edgeboundaries for the cell at different times in response to analytes. Edgeboundary measurement with software programs is well known. Using themicroscope in combination with the sensor and processor detecting amechanical deformation in the membrane of a cell associated with themolecular interactions can be performed as desired.

Referring now particularly to FIG. 1B, differential optical detectionfor accurate tracking of cell edge change induced by analyte receptorinteraction is illustrated. The edge of a cell 5 can be manually definedfrom the optical image of the cell, but an imaging processing algorithmcan automatically detect the cell edge, providing high throughput. Afterdetermining the cell edge, a region of interest (ROI) is selected at alocation of the cell edge. The ROI can be a rectangular shape30 with thecell edge 32 passing through the center portion of the rectangle,dividing the rectangle into two equal halves, one half 34 is inside ofthe cell, and the second half 36 falls outside of the cell. We denotethe intensities of the two halves of the differential image as A and B,and (A−B)/(A+B) is calculated and used to determine the movement of thecell edge at each location. We refer (A−B)/(A+B) to as differentialimage intensity, and this method of edge movement tracking differentialoptical detection.

An alternative approach is to first determine a differential imagewithin the ROI by shifting the image within the ROI perpendicular to thecell edge by a small distance (e.g., 1 pixel), and then calculating thedifference between the shifted image and the original image pixel bypixel. We denote the intensities of the two halves of the differentialimage as dA and dB, and (dA−dB)/(dA+dB) is calculated and used todetermine the movement of the cell edge at a given location. This methodof edge movement tracking is also referred to as differential opticaldetection.

The relation between the cell edge movement and the measureddifferential image intensity change from one of the above differentialoptical detection method can be determined as below. The pixel densityof each image is enlarged 5 times by adding additional pixels with abilinear interpolation approach. The distance between two pixels in theinterpolated image corresponds to a real distance of the object (cell)is 37 nm (Pike F032B CCD, Allied Vision Technologies, Stadtroda,Germany). The edge of one cell is manually chosen and the centroid (O)of the cell is determined (FIGS. 1B and 1C). A polar coordinate systemis setup with the centroid serving as the pole. The cell edge movementwas calculated at every 10 starting from 0 (FIG. 1E). The ROI at acertain point of the cell edge (point A) is then shifted by differentnumbers of pixels outwards (perpendicular to the tangential line atpoint A), and the corresponding changes in the differential imageintensity is determined from the image. The relation between thedifferential image intensity and the cell edge movement (pixels) isfound to be linear within a certain range, which serves as a calibrationcurve to determine the cell edge movement (mechanical deformation) fromthe differential image intensity. This differential optical detectionmethod can accurately detect cell edge movement with a detection limitas small as 0.5 nm, the size of an atom (FIG. 1D).

EXAMPLES

Referring now jointly to FIG. 2A-FIG. 2D, there shown are illustrativedrawings of WGA interaction with glycoproteins. White arrows 40, 42 markthe cells under analysis in FIG. 2A and FIG. 2B respectively. The scalebar for FIGS. 2C and 2D is 10 μm. FIG. 2C graphically illustrates anexample for averaged cell edge movement over the whole cell for 20 g/mlWGA. FIG. 2D graphically illustrates an example for averaged cell edgemovement over the whole cell for 5 g/ml WGA. The scale bar for FIGS. 2Cand 2D is 10 μm. The Y axis for FIGS. 2C and 2D represents edge movementin nm ranging from −5 to 25 nm. The X axis represents time in seconds.

The following examples demonstrate the detection of the binding of bothlarge and small molecules with membrane receptors in cells using thesystem and method in this system. An inverted microscope (Olympus X81)equipped with phase 2 condenser and phase 2 40× objective was used withillumination from the top of the sample cells.

Large Molecule-membrane Receptor Protein Interactions

To demonstrate the capability of the system and method for detecting andanalyzing molecular binding of membrane receptors of cells, the bindingkinetics of wheat germ agglutinin (WGA) and glycoprotein on Barrett'sesophagus derived CP-D (CP-18821) cells was studied. WGA is one kind oflectins that binds to N-Acetyl glucosamine (GlcNAc) and sialic acidgroups. The CPD cells were fully attached onto a glass slide.

FIGS. 2A and 2B show the phase contrast images of attached fixed CP-Dcells. The measurement was carried out by flowing 1×PBS over the cellsfor 30 s with a flow rate of 350 L/min to obtain the baseline. At time 0s, WGA solution in 1× PBS was introduced for 90 s allowing theassociation (binding) of WGA with CP-D glycoprotein on the cell surface.During the association process, the cell edge moves outwards as shown inFIGS. 2C and 2D. Then WGA solution was switched to 1×PBS to allow thebound WGA to dissociate from the CP-D cells at time 90 s. FIGS. 2C and2D show that the cell edge moves back to the original position duringthe dissociation process. By fitting the data with the first orderkinetics, association rate constants (k_(on)), dissociation rateconstants (k_(off)), and dissociation constant (K_(D)) were found to be

-   -   k_(on)=1.4×10⁵ M⁻¹ s⁻¹, k_(off)=2.9×10⁻³s⁻¹ s⁻¹, K_(D)=0.021 μM        for 20 ug/ml WGA and k_(on)=1.09×10⁵ M⁻¹ s⁻¹, k_(off)=1.5 6×10⁻³        s⁻¹, K_(D)=0.014 μM for 5 ug/ml WGA.        Small Molecule-membrane Receptor Protein Interactions

In order to demonstrate small molecule binding to single cells with thepresent system, the binding of acetylcholine with nicotinicacetylcholine receptors (nAChRs) was studied. Engineered SH-EP1 cellsthat expressed human α4β2 receptors were used to examine the bindingkinetics of acetycholine with nAChRs. Human α4β2 mainly exists in thebrain and is related to the nicotine addiction. Study of the nAChRs andsmall molecule interactions is important for revealing the mechanism ofnicotine addiction as well as development of drugs to treat nicotineaddiction.

Referring now jointly to FIG. 3A-FIG. 3C, acetylcholine interaction withnicotinic acetylcholine receptors in cells, specifically a phasecontrast image of the fixed human a4β2 transfected SH-EP1 cells. Thephase contrast image of fixed SH-EP1-hα4β2 cells is shown in FIG. 3A,where the white arrow 50 marks the cell under analysis. The scale bar is10 μm.

1×PBS buffer was continuously flowing over the cell for 25 seconds toobtain a stable baseline. At time 0 s, the buffer was changed toacetylcholine solution of different concentrations in 1×PBS. Afterassociation, the acetylcholine solution was switched back to 1×PBS toallow dissociation.

Referring now particularly to FIG. 3B averaged cell edge movement overthe whole cell and fitting results during the binding process foracetylcholine of different concentrations is shown. The Y axisrepresents edge movement in nm ranging from about 0 to 40 nm. The X axisrepresents time in seconds. Averaged cell edge movement over the wholecell is indicated by the experimental data points 70 fitted results areshown as a plurality of curves 72. From bottom to top the buffer(negative control), was 0.5 μM, 2 μM, 5 μM, and 50 μM.

As shown in FIG. 3B, the cell edge moves outwards during the associationphase and retracts back during the dissociation phase. It also showsthat the amount of cell edge movement during the association processincreases with the acetylcholine concentration, which is expected forfirst-order reaction kinetics. The association rate constant (k_(on))and dissociation rate constant (k_(off)) were found to be 9.4×10³ M⁻¹s^(−1A)nd 2.47×10⁻² s⁻¹ respectively, from which the dissociationconstant (K_(D)) was determined to be 2.7 μM.

Referring now particularly to FIG. 3C, there illustrated is an examplewherein the equilibrium constant (K_(D)) was determined to be 2.601 μMby plotting the edge movement vs. acetylcholine concentration andfitting the data with the Langmuir isotherm. The Y axis represents edgemovement in nm ranging from about 0 to 40 nm. The X axis representsconcentration (M) ranging from about 10⁻⁷ to 10⁻⁴ M.

Referring now jointly to FIG. 3D-FIG. 3F, acetylcholine interaction withnicotinic acetylcholine receptors in cells, specifically a phasecontrast image of the fixed human a4β2 transfected SH-EP1 cells. Thephase contrast image of fixed SH-EP1-hα4β2 cells is shown in FIG. 3D,where the white arrow 350 marks the cell under analysis. The scale baris 20 μm. 1×PBS buffer was first introduced to flow over the cell for 25s, and then the buffer was switched to an acetylcholine solution in1×PBS. After association, the acetylcholine solution at eachconcentration was switched back to 1×PBS to allow for dissociation. Theabove procedure was repeated for different acetylcholine concentrations.

As shown in FIG. 3E, the cell edge expands during the association phaseand retracts during the dissociation phase. FIG. 3E also shows that theamount of cell expansion during the association process increases withthe acetylcholine concentration, which is expected for first-orderbinding kinetics. The association (kon) and dissociation (koff) rateconstants were found to be 1.2×10⁻⁶ M⁻¹ s⁻¹ and 2.2×10⁻² s⁻¹,respectively, which represent the first direct measurement of thekinetic constants for the binding of the neurotransmitter to the nAChRsin intact cells.

From kon and koff, the equilibrium dissociation constant (KD=koff/kon)was determined to be 18.1 nM. By plotting the equilibrium responseversus acetylcholine concentrations (FIG. 3F), the equilibrium constant(KD) was found to be ˜26 nM, which is consistent with that obtained bykinetics measurement. As this is the first kinetic measurement ofacetycholine binding to nAChRs, the findings cannot be compared to otherreference technologies or prior data. However, the equilibriumdissociation constant determined here is in good agreement with theaverage Ki determined with radioligand binding assay, which involvedcentrifuge and formation of cell pellets.

Referring now to FIG. 3G, phase contrast images of wild type SH-EP1cells G,

H are shown. The scale bar is 20 μm. As a control experiment, themeasurement was carried out with wild type SH-EP1 cells, which do nothave nAChRs expressed on the cell surfaces, and observed no deformationin the cell membrane. This result demonstrated that the mechanicaldeformation in the engineered SH-EP1 cells was indeed due to thespecific binding of acetylcholine to the expressed nAChRs.

Referring now to FIG. 3H, response of wild type SH-EP1 cells to 500 nMacetylcholine is graphically shown. Arrow 350 marks the switch from1×PBS to 500 nM acetylcholine and the arrow 352 indicates the change ofsolution back to 1×PBS. Curve 370 represents the measurements for cell Gand lighter curve 370 represents the measurements for cell H.

Heterogeneity

Referring now jointly to FIG. 4A-FIG. 4F, heterogeneity of smallmolecule interactions with cell membranes are illustrated. The presentsystem makes it possible to examine the cell-cell variation andheterogeneity within a cell. FIGS. 4A and 4C are the phase contrastimages of fixed SH-EP1-hα4β2 cells and numbers in circles mark the cellsunder analysis. The scale bar is 10 μm. Cell 1 and cell 2 are on thesame glass slide, and cell 3 and cell 4 are on a different glass slide.The responses of four cells to 50 -μM acetylcholine are shown in FIGS.4B and 4D, and the corresponding kinetic constants are given in Table 1,which show significant differences in the binding kinetics. Differentregions on a cell edge also show variations in the binding kinetics(FIGS. 4E and 4F). The scale bar is 10 μm. Insets 55, 65 show phasecontrast images of fixed human α4β2 transfected SH-EP1 cells recorded atthe time 0 s and 68 s, respectively.

Referring particularly to FIG. 4B, binding kinetics of cells 1 and 2 aregraphed. The Y axis represents cell edge movement in nm and the X-axisrepresents time in seconds. Curve 470 represents fitted results forexperimental data 472 for cell 1. Curve 473 represents fitted resultsfor experimental data 474 for cell 2. Similarly, FIG. 4D shows bindingkinetics of cells 3 and 4. The Y axis represents cell edge movement innm and the X-axis represents time in seconds. Curve 483 representsfitted results for experimental data 484 for cell 3. Curve 480represents fitted results for experimental data 482 for cell 4.

TABLE 1 Association rate constants (k_(on)), dissociation rate constants₍k_(off),) and equilibrium constants (K_(D)) for four cells as shown inFIGS. 4A-4D. k_(on) (M⁻¹s⁻¹) k_(off) (s⁻¹) K_(D)(μM) Cell 1 1.00 × 10³0.0152 15.1 Cell 2 5.02 × 10² 0.0139 28.1 Cell 3 4.01 × 10² 0.0142 36.1Cell 4 1.26 × 10³ 0.0150 11.9

Referring now to FIG. 5A and FIG. 5C phase contrast images of fixedhuman α4β2 transfected SH-EP1 cells are shown. Cells under analysisinclude cells 5-7. The scale bars for both figures is 20 μm.

Referring to FIG. 5B, binding kinetics of cells 5 and 6 are graphed. TheY axis represents cell edge movement in nm and the X-axis representstime in seconds. Curve 570 represents fitted results for experimentaldata 572 for cell 5. Curve 573 represents fitted results forexperimental data 574 for cell 6. Similarly, FIG. 5D shows bindingkinetics of cells 7 and 8. The Y axis represents cell edge movement innm and the X-axis represents time in seconds. Curve 583 representsfitted results for experimental data 584 for cell 7. Curve 580represents fitted results for experimental data 582 for cell 8.

TABLE 2 Association rate constants (kon), dissociation rate constants(koff), and equilibrium constants (KD) for four cells as shown in FIG.5A-FIG. 5D. kon (M⁻¹s⁻¹) koff (s⁻¹) KD (nM) Cell 5 7.32 × 10⁵ 0.016122.0 Cell 6 8.33 × 10⁵ 0.0217 26.1 Cell 7 3.42 × 10⁵ 0.0298 87.1 Cell 85.76 × 10⁵ 0.0172 29.9

Referring to FIG. 5E binding kinetics at different locations of cell(100 nM Acetylcholine) is graphically illustrated. The Y axis representscell edge movement in nm and the X-axis represents time in seconds.

Referring to FIG. 5F, a phase contrast image of fixed human α4β2transfected SH-EP1 cell is shown. The amount of cell membranedeformation after acetylcholine binding ranges from about 80 nm, forexample, at arrow 590 to about −40 nm, at, for example, region 592(scale bar: 20 μm).

Experimental Details

Materials

Wheat germ agglutinin (WGA) and acetylcholine chloride were purchasedfrom Sigma-Aldrich (St. Louis, Mo.). 1× phosphate buffer saline (PBS)pH=7.4 was used as buffer for all binding experiments. All samples wereprepared in 1×PBS buffer.

Cell Culture

The human α4β2 transfected human epithelial SH-EP1 cells were culturedin a humidity incubator at 37 with 5% CO2 and 70% relative humidity.Dubelco's Modified Eagle's Medium (DMEM, Lonza, Walkersville, Md.) with10% Fetal Bovine Serum (FBS, Life Technologies, Carlsbad, Calif.) andpenicillin-streptomycin (BioWhittaker, Basel, Switzerland) were used asculture medium. SH-EP1-h α4β2 cells were cultured in 25 cm² flask untilapproximately 80% confluence was reached for passage. 0.05% trypsin-EDTA(Life Technologies, Carlsbad, Calif.) was used for cell passage.

The CP-D cell were cultured in an incubator at 37 with 5% CO2 and 70%relative humidity. Cells were cultured in 25 cm² flask with 1×Keratinocyte-SFM (Life Technologies, Carlsbad, Calif.) andpenicillin-streptomycin (BioWhittaker, Basel, Switzerland) as culturemedium. When cells reached approximately 80% confluent, cells werepassaged with 0.05% trypsin-EDTA (Life Technologies, Carlsbad, Calif.).

For experiments, cells were cultured overnight on the bare glass slides(22×60 mm micro cover glass, VWR, Radnor, Pa.) in a silicone well(FlexiPERM, Greiner bio-one, Monroe, N.C.) placed on top of it in orderto let cells attach on the surface. Cells on glass slides were alsocultured in the incubator at 37 with 5% CO2 and 70% relative humidity.Cells were incubated in 4% paraformaldehyde for 10 μmin at roomtemperature for fixation and then ready for experiments. Before themeasurement, the small silicone well was changed to a homemade PDMS wellwith 2 cm in length, 1 cm in width and 1 cm in height.

High Throughput Analysis of Individual Cells with MinimizedMicro-motions

As discussed in detail above, thermodynamic principles (Eqs. 1 and 2)predict that a mechanical deformation always accompanies a molecularbinding on a cell surface. This is the basic principle of MADMI.However, thermodynamics does not bring to light how much a cell willdeform for a given molecular binding. This information is not needed forbinding kinetic constant measurement because the kinetics constants canbe determined from fitting the relative mechanical deformation vs. timeplot with a kinetics model. Precise prediction of the amount ofmechanical deformation associated with a binding event would requiremolecular scaled knowledge about the nature of the molecule-membraneprotein interactions, as well as the surface density of the membraneproteins, and mechanical property of the cell. Obtaining such detailedknowledge and analyzing MADMI data in terms of the microscopic knowledgeare beyond the scope of the present project. However, it is believedthat the amount of mechanical deformation in MADMI increase with thebinding strength and surface density of the target membrane proteins.

Referring now to FIG. 6A, trapping of cells onto the holes of amicroplate is schematically shown. To minimize micro-motion of livecells and achieve high throughput analysis of molecular bindingkinetics, a method to trap individual cells 601 for real-time study ofmolecular binding kinetics of membrane proteins using MADMI will use amicroplate 600 with an array of μm-scaled holes 605, such thatindividual cells 601 can be trapped onto the holes with an appliedpressure difference across the holes. Preliminary results show thatMADMI works well with adherent cells. At least one cell 601 will betrapped onto a hole 603 via a negative pressure created with a laminarflow 605 underneath the microplate 600. An objective lens 612 may beused for obtaining images. A similar method has been used in commercialautomated patch clamping systems, where micron-sized holes are createdin the bottom of the wells of a microplate.

Referring now jointly to FIG. 6B-FIG. 6E, apparatus for trapping cellsis illustrated. FIG. 6B is a photo of a 96 well automated patch clampplate 600 of an lonFlux Automated Patch Clamp System. FIG. 6C is anexpanded view showing the fluidic design and function of each well inthe lonFlux Automated Patch Clamp System of FIG. 6B. FIG. 6D is a moreexpanded view of a portion of the lonFlux Automated Patch Clamp Systemshowing the cell-trapping zone 610. FIG. 6E shows an image of trappedcells. IonFlux Automated Patch Clamp Systems are available from FluxionBiosciences, Inc., South San Francisco, Calif. 94080.

Referring now to FIG. 7A, a bright field image of a floating cellclamped with glass micropipette is shown. A cell 701 is physicallyconnected with micropipette 704 in a whole-cell patch configuration. Thewhite spot 705 in the center of the cell is the tip of the micropipette.As a preliminary test to examine the feasibility of the method, a cellwas trapped with a micropipette used in patch clamp. An AC electricalfield having a frequency of 37 Hz was then applied across the cellularmembrane to create a periodic mechanical deformation in the membrane.

Referring now to FIG. 7B, a cell 701 modulated by an external applied ACfield from micropipette 704 is shown. The electromechanical couplinginduced mechanical deformation was precisely measured by MADMI methodand visualized with nanometer precision ranging from 0 to about 5 nm.The mechanical deformation occurred because of an electromechanicalcoupling effect, which was precisely measured by MADMI with nanometerprecision. This preliminary test shows that it is possible to trap acell to reduce its micro-motions, and to detect the mechanicaldeformation of the cell membrane with nanometer precision.

With the trapping of the cells, cellular micro-motions may bedeteremined by performing noise spectrum analysis, and studying themicro-motions by applying different negative pressure, and effect of thesize of the holes. MADMI may then be applied to determine molecularinteraction kinetics using both large and small molecules, and comparethe results with those of the adherent cells. Based on the finding, thecell trapping method may be optimized by tuning the diameter, flow rateand shear force for rapid cell trapping, in order to minimize themicro-motions and maximizing the throughput.

A low-noise imaging technology for studying low-density membraneproteins with MADMI. The density or abundance of the membrane proteinson cell surface varies over several orders of magnitudes (˜10³-10⁸protein/cell, or 1-10⁵ protein/μm²). Since the binding signal isproportional to the membrane protein receptor density, in order to studylow-density membrane proteins with MADMI, it is necessary to improve thedetection limit of the cell membrane deformation. To achieve this goal,a low noise z-modulation Fast Fourier Transform (FFT) optical imagingtechnology may be used as described below. The FFT technology willperiodically modulate the sample along the optical axis (z-axis) tocreate a periodic modulation in the focus, and determines the associatedimage contrast modulation with FFT, which minimizes noise and providessuperior detection limit. We expect to reach a detection limit of 0.25nm for the membrane deformation. Such a detection limit will help detectthe bindings of molecules to low-density membrane proteins, e.g., 10proteins/μm².

The z-modulation FFT microscope will improve the signal to noise ratioby modulating the sample stage vertically (z-axis). This is possiblebecause the z-modulation FFT microscope will detect signals associatedwith the sample only, and it reduces noise from light source, optics andcamera, and also removes effects due to defects or dirt in the optics ofthe microscope. Additionally, the periodic modulation will alloweffective use of FFT filter to remove all types of noise that hasdifferent frequencies from the modulation frequency. The z-modulationmicroscope may advantageously be built on a traditional bright fieldoptical microscope with an attached z-piezo modulation sample holder andproper imaging-processing algorithm. A similar method was previousdemonstrated by Gineste et al.²⁵ An important difference is that thesystem disclosed herein will use periodic modulation and Fouriertransform algorithm to reduce noise in the image.

The basic principle of z-modulation FFT microscopy is described by theTransport of the Intensity Equation (TIE) according to the formula

$\begin{matrix}{{{{- k}\frac{\partial{I( {\overset{arrow}{r_{\bot}},0} )}}{\partial z}} = {\nabla_{\bot}{\cdot \lbrack {{I( {\overset{arrow}{r_{\bot}},0} )}{\nabla_{\bot}{\phi( {\overset{arrow}{r_{\bot}},0} )}}} \rbrack}}},{k = \frac{2\pi}{\lambda}}} & (3)\end{matrix}$where I({right arrow over (r_(⊥))}, z) and ϕ({right arrow over (r_(⊥))},z) are the intensity and phase at the position at ({right arrow over(r_(⊥))}, z) respectively, z denotes position along the optical axis and{right arrow over (r_(⊥))} denotes position within a plane normal to theoptical axis, and λ is the wavelength and k is the wavenumber. Thisequation relates the rate of change of intensity in the direction of theoptical axis to the intensity and phase of light in a planeperpendicular to the optical axis. When I({right arrow over (r_(⊥))}, 0)is constant, I₀, Eq. 3 can be simplified as

$\begin{matrix}{{\frac{\partial{I( {\overset{arrow}{r_{\bot}},0} )}}{\partial z} = {{- \frac{I_{0}}{k}}( {\nabla_{\bot}^{2}{\phi( {\overset{arrow}{r_{\bot}},0} )}} )}},} & (4)\end{matrix}$where the left of the equation is the z-modulation FFT microscopy imagecontrast, and the right side is the spatial Laplacian, ∇_(⊥) ², (secondderivative) of image phase, ϕ({right arrow over (r_(⊥))}, 0), at thefocus. Eq. 4 shows that the z-modulation FFT microscopy image contrastis proportional to the second derivative of the phase of the object. Forthis reason, z-modulation FFT microscopy provides excellent imagecontrast, which allows accurate detection of the cell deformation.

Referring now to FIG. 8A the setup of z-modulation FFT microscopy isshown. The sample, and thus the focal plane, is modulated verticallywith a piezoelectric transducer driven by a periodic voltage 815, andbright field images are recorded sequentially.

Referring now to FIG. 8B, to obtain the focus modulation image, temporalFast Fourier Transform (FFT) was applied to the bright field imagesequences 820 over a time window 822. Amplitude data 830 and phase data832 can thus be obtained.

Referring now to FIG. 8C, a periodically modulated image intensity of apixel, and its corresponding FFT spectrum, where a peak is located atthe frequency of modulation are shown. Plot 802 is a time plot ofmodulated image intensity. FFT intensity spectrum for a backgroundregion 804 and a sample region 805 with focus modulation at 37 Hz isshown. A peak 810 appears in the FFT spectrum at the modulationfrequency for the sample region, but not in the background region. Theamplitude of the peak 810 was extracted for each pixel to create az-modulation FFT microscopy image.

Referring now jointly to FIG. 8D and FIG. 8E, to evaluate noisereduction capability of z-modulation FFT microscopy, 42 nm polystyrenenanoparticle (PSNP) were imaged on a glass surface. Individual 42 nmpolystyrene nanoparticles are invisible in bright field image (FIG. 8D),but are clearly visible in the modulation image as marked by arrows 830(FIG. 8E). Similarly, background noise in the bright field image (FIG.8D) of cells is removed (FIG. 8G) and detailed cellular structures areenhanced in the z-modulation FFT image. FIG. 8D shows the traditionalbright field image of the same sample, which cannot resolve thenanoparticles due to noise. In contrast, z-modulation FFT microscopy canclearly resolve individual 42 nm PSNPs, as shown in FIG. 8E. Anotherexample is cultured cells. The z-modulation FFT image (FIG. 8G) showssuperior contrast compared with the traditional bright field imageobtained with same camera (FIG. 8F). Individual 42 nm polystyrenenanoparticles are invisible in bright field image (FIG. 8D), but areclearly visible in the modulation image as marked by arrows 830 (FIG.8E). Similarly, background noise in the bright field image (FIG. 8D) ofcells is removed (FIG. 8G) and detailed cellular structures are enhancedin the z-modulation FFT image.

Referring now jointly to FIG. 9A and FIG. 9B, images from a preliminarytest of WGA binding kinetics on a SHEP1 cell with the z-modulation FFTmicroscope are shown. FIG. 9. WGA binding kinetic measured byz-modulation FFT microscopy. FIG. 9A Bright field image of fixed SHEP1cell; FIG. 9B z-modulation FFT image of the same cell; FIG. 9C Bindingkinetic plots of 10, 20 and 50 μg/mI WGA (from bottom to top curves).The plots were obtained by tracking the intensity changes along the edgeof the modulation amplitude image. Measured kinetic constants:k_(on)=1.4×10⁴ M⁻¹s⁻¹, k_(off)=0.013 s⁻¹, K_(D)=125 nM. Scale bar: 10μm. The kinetic curves were obtained from the amplitude of the imagesequences along the edge of a cell. Kinetic constants and bindingaffinity were obtained by fitting the kinetic curves with first orderkinetic model.

The z-modulation FFT imaging technology together with the differentialoptical algorithm may be used to study molecular binding kinetics ofmembrane proteins with different expression levels. One of the modelsystems planned for study is human epidermal growth factor receptor(EGFR, or HER1), which interacts with target protein drug, Panitumumab(Vectibix, FDA approved recombinant human IgG2k mAb drug manufactured byAmgen). EGFR plays an essential role in regulating normal cellsignaling, and the mutation of EGFR leads to cell proliferation,angiogenesis, invasion, metastasis and inhibition of apoptosis,accounting for the pathogenesis and progression of cancer cells (33-37).Monoclonal antibodies targeting the extracellular do-main of EGFR havebeen used in various stages of pre-clinical development, and have showngood therapeutic efficacy for treatment of a number of cancers that haveup-regulated EGFR expression level. The kinetic constants of the bindingof these antibody drugs to EGFR receptors are the key parameters tocharacterize the efficacy of these drugs. We will use cell lines withdifferent EGFR expression levels to measure the antibody druginteraction kinetics, and to evaluate the detection limit of MADMI.

Cell lines with different expression levels of EGFR, including A431(high), Hela (medium) and A549 (low), and HEK293 (negative control) willbe used. The EGFR expression levels of these cells have been confirmedin our lab using quantitative SPR imaging and immunofluorescenceimaging. The membrane densities of EGFR in A431, Hela and A549 cellscalculated from maximum SPR binding signals are 640, 270 and 140receptors/ μm², respectively.

Binding kinetic curves of different doses of Panitumumab antibody withthese four cell lines will be measured by MADMI. Kinetic constants willbe obtained through globe fitting of the binding kinetics curves, andEGFR expression level will be quantified from the maximum bindingsignal.

The invention has been described herein in considerable detail in orderto comply with the Patent Statutes and to provide those skilled in theart with the information needed to apply the novel principles of thepresent invention, and to construct and use such exemplary andspecialized components as are required. However, it is to be understoodthat the invention may be carried out by different equipment, anddevices, and that various modifications, both as to the equipmentdetails and operating procedures, may be accomplished without departingfrom the true spirit and scope of the present invention.

REFERENCES

The following publications are incorporated by reference.

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What is claimed is:
 1. A system for quantitative detection and analysisof the binding of molecules with molecular receptors on the surfaces ofa biological object, the system comprising: a microscope positioned toimage the biological object and transmit a time sequence of images ofthe biological object; a camera located to receive the transmitted timesequence of images of the biological object from the microscope andtransmit data representing the time sequence of images; a processor,coupled to receive the data representing the time sequence of imagesfrom the camera; the processor including an image processing algorithmthat quantitatively determines mechanical deformation of the biologicalobject from the time sequence of images, wherein the mechanicaldeformation is due to an interaction of the biological object with aplurality of molecules, wherein the image processing algorithmdetermines mechanical deformation by using an edge detector to determinean edge of the biological object and a time sequence tracker fortracking a plurality of movements of the edge by measuring positions ofthe edge at different times, and wherein the image processing algorithmalso determines molecular binding kinetic values of the biologicalobject as proportional to the mechanical deformation of the biologicalobject.
 2. The system of claim 1 wherein the time sequence tracker fortracking the plurality of movements of the edge of the biological objectcomprises a differential image intensity analysis algorithm.
 3. A methodfor quantitative detection and analysis of the binding of molecules withmolecular receptors on the surfaces of a biological object, the methodcomprising: operating a microscope and a sensor to capture a timesequence of images of a biological object; operating a processor toquantitatively determine the mechanical deformation of the biologicalobject from the time sequence of images due to interaction of thebiological object with a plurality of molecules, wherein determining themechanical deformation comprises operating an edge detector fordetermining an edge of the biological object and tracking a plurality ofmovements of the edge by measuring positions of the edge at differenttimes; and operating the processor to determine molecular bindingkinetic values of the biological object as proportional to themechanical deformation of the biological object and to determine bindingkinetic constants, including ka, kd and KD from the time sequence ofimages.
 4. The method of claim 3 wherein tracking the plurality ofmovements of the edge of the biological object further comprises runninga differential image intensity analysis algorithm on the processor. 5.The method of claim 3 wherein the biological object is a cell and theprocessor operates to measure the plurality of movements of the edge ofthe biological object by: detecting a cell edge from an optical image ofthe cell; selecting a region of interest (ROI) at a location of the celledge; dividing the ROI into two equal halves, wherein one half is insideof the cell, and wherein the second half falls outside of the cell; anddetermining the edge movement by computing (A−B)/(A+B), where A and Bare the intensities of the two halves of the image.
 6. A system forquantitative detection and analysis of binding of neuron stimulatormolecules with molecular receptors on the surfaces of a neuron, thesystem comprising: a microscope positioned to image the neuron andtransmit a time sequence of images of the biological object; an imagesensor located to receive transmitted images of the neuron from themicroscope and transmit data representing the time sequence of images; aprocessor coupled to receive the data representing the time sequence ofimages from the image sensor, the processor including an imageprocessing algorithm that quantitatively determines mechanicaldeformation of the neuron from the time sequence of images, wherein themechanical deformation is due to interaction of the neuron with aplurality of neuron stimulator molecules, wherein the image processingalgorithm determines mechanical deformation by using an edge detector todetermine an edge of the biological object and a time sequence trackerfor tracking a plurality of movements of the edge by measuring positionsof the edge at different times, and wherein the image processingalgorithm also determines molecular binding kinetic values of the neuronas proportional to the mechanical deformation of the neuron.
 7. A methodfor quantitative detection and analysis of the binding of neuronstimulator molecules with molecular receptors on the surfaces of aneuron, the method comprising: operating a microscope and a sensor tocapture a time sequence of images of a biological object; operating aprocessor to quantitatively determine mechanical deformation of theneuron from the time sequence of images due to interaction of the neuronwith a plurality of neuron stimulator molecules, wherein determining themechanical deformation comprises operating an edge detector fordetermining an edge of the biological object and tracking a plurality ofmovements of the edge by measuring positions of the edge at differenttimes; and operating the processor to determine molecular bindingkinetic values of the neuron as proportional to the mechanicaldeformation of the neuron.